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Training and deploying machine learning models relies on a large amount of human-annotated data.
Large language models in medicine
Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. 2023 · 1940
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Rating the ratings: Assessing the psychometric quality of rating data
Frank E Saal, Ronald G Downey, and Mary A Lahey. 1980 · 1980
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A sequential algorithm for training text classifiers
David D. Lewis and William A. Gale. 1994 · 1994
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
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Active learning to recognize multiple types of plankton
Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, and Thomas Hopkins. 2005 · 2005
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Uncertainty-aware self-training for text classification with few labels
Subhabrata Mukherjee and Ahmed Hassan Awadallah. 2020 · 2006
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A survey of active learning for text classification using deep neural networks
Christopher Schröder and Andreas Niekler. 2020 · 2008
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Best practices for managing data annotation projects
Tina Tseng, Amanda Stent, and Domenic Maida. 2020 · 2009
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Corpus annotation through crowdsourcing: Towards best practice guidelines
Marta Sabou, Kalina Bontcheva, Leon Derczynski, and Arno Scharl. 2014 · 2014
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Introduction: The Handbook of Linguistic Annotation
Nancy Ide and James Pustejovsky. 2017 · 2017
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2020 · 2020
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Édouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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Active learning for bert: an empirical study
Liat Ein Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2020 · 2020
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Crowdsourcing practice for efficient data labeling: Aggregation, incremental relabeling, and pricing
Alexey Drutsa, Valentina Fedorova, Dmitry Ustalov, Olga Megorskaya, Evfrosiniya Zerminova, and Daria Baidakova. 2020 · 2020
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Structured pruning of large language models
Ziheng Wang, Jeremy Wohlwend, and Tao Lei. 2020 · 2020
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Crowdsourcing natural language data at scale: A hands-on tutorial
Alexey Drutsa, Dmitry Ustalov, Valentina Fedorova, Olga Megorskaya, and Daria Baidakova. 2021 · 2021
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Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021 · 2021
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Crowdsourcing beyond annotation: Case studies in benchmark data collection
Alane Suhr, Clara Vania, Nikita Nangia, Maarten Sap, Mark Yatskar, Samuel R. Bowman, and Yoav Artzi. 2021 · 2021
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Putting humans in the natural language processing loop: A survey
Zijie J Wang, Dongjin Choi, Shenyu Xu, and Diyi Yang. 2021 · 2021
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Jury learning: Integrating dissenting voices into machine learning models
Mitchell L Gordon, Michelle S Lam, Joon Sung Park, Kayur Patel, Jeff Hancock, Tatsunori Hashimoto, and Michael S Bernstein. 2022 · 2022
Cited alongside, same era.
The curse of recursion: Training on generated data makes models forget
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson. 2023 · 2023
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To aggregate or not? learning with separate noisy labels
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo, Ehsan Amid, Abhishek Kumar, and Yang Liu. 2023 · 2023
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LLMaAA: Making Large Language Models as Active Annotators
Ruoyu Zhang, Yanzeng Li, Yongliang Ma, Ming Zhou, and Lei Zou. 2023 · 2023
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Bias and fairness in large language models: A survey
Isabel O Gallegos, Ryan A Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K Ahmed. 2024 · 2024
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Analyzing dataset annotation quality management in the wild
Jan-Christoph Klie, Richard Eckart de Castilho, and Iryna Gurevych. 2024 · 2024
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Chaofan Tao, Lu Hou, Wei Zhang, Lifeng Shang, Xin Jiang, Qun Liu, Ping Luo, and Ngai Wong. 2022 · 2022
Cited alongside, same era.
A survey of human-in-the-loop for machine learning
Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, and Liang He. 2022 · 2022
Cited alongside, same era.
Zerogen: Efficient zero-shot learning via dataset generation
Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, and Lingpeng Kong. 2022 · 2022
Cited alongside, same era.
A survey of active learning for natural language processing
Zhisong Zhang, Emma Strubell, and Eduard Hovy. 2022 · 2022
Cited alongside, same era.
How to work with real humans in human-ai systems
Elizabeth Bondi-Kelly, Krishnamurthy Dvijotham, and Matthew Taylor. 2023 · 2023
Cited alongside, same era.
Can ai language models replace human participants?
Danica Dillion, Niket Tandon, Yuling Gu, and Kurt Gray. 2023 · 2023
Cited alongside, same era.
A survey of language model confidence estimation and calibration
Jiahui Geng, Fengyu Cai, Yuxia Wang, Heinz Koeppl, Preslav Nakov, and Iryna Gurevych. 2023 · 2023
Cited alongside, same era.
Ai models collapse when trained on recursively generated data
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, and Yarin Gal. 2024 · 2024
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Large language models for data annotation: A survey
Zhen Tan, Alimohammad Beigi, Song Wang, Ruocheng Guo, Amrita Bhattacharjee, Bohan Jiang, Mansooreh Karami, Jundong Li, Lu Cheng, and Huan Liu. 2024 · 2024
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The llama 3 herd of models
Llama team. 2024 · 2024
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LLMs & humans: The perfect duo for data labeling
Sergei Tilga. 2024 · 2024
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Learning from crowds with crowd-kit
Dmitry Ustalov, Nikita Pavlichenko, and Boris Tseitlin. 2024 · 2024
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Seaeval for multilingual foundation models: From cross-lingual alignment to cultural reasoning
Bin Wang, Zhengyuan Liu, Xin Huang, Fangkai Jiao, Yang Ding, Aiti Aw, and Nancy Chen. 2024a · 2024
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Hallucination is inevitable: An innate limitation of large language models
Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli. 2024 · 2024
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Human-AI interaction in the age of LLMs
Diyi Yang, Sherry Tongshuang Wu, and Marti A. Hearst. 2024 · 2024
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Large language model as attributed training data generator: A tale of diversity and bias
Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander J Ratner, Ranjay Krishna, Jiaming Shen, and Chao Zhang. 2024 · 2024
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